The Data and Images of Natural Disaster News
Report Based on Artificial Intelligence Technology
Processing
C
hunxiao Wang
Computer Science
City University of Hong Kong
Beijing, China
cx894448636@163.com
Ruiqi Peng
Data Journalism
Communication University of China
Beijing, China
2878572825@qq.com
A
bstractIn recent years, the technology of writing data news
report by using artificial intelligence has been rapidly developed.
The news that is generated automatically has strong timeliness.
However, due to lack of humanistic care, it can't be widely used in
long reports. Based on the collection and analysis ability on data
of artificial intelligence, the data news can be completed by
program algorithms and machine learning. By taking use of
natural disaster data extracted by crawler, and achieving
recognition and classification of disaster weather warning images
by convoluted networks, this paper comes up with the model of
data and images in the natural disaster news report based on
artificial intelligence technology processing.
Keywordsartificial intelligence, natural disaster, data, images
I. I
NT
RODUCTION
A
. Research Background
At 21:19 on August 8, 2017, a magnitude 7.0 earthquake
occurred in Jiuzhaigou County, Aba Prefecture, Sichuan
Province. Within 18 minutes after earthquake occurrence, the
first news a magnitude 7.0 earthquake occurred in Jiuzhaigou
County, Aba Prefecture, Sichuan Provincewas pushed by
WeChat official account China Seismological Network, of
which the contents contained quick report parameters, epicenter
topography, thermal population, historical earthquake, epicenter
weather and others. This report was generated by computer
taking 7 seconds to complete. Pan Huaiwen, the director of the
China Earthquake Network Center, pointed out that the robot
enters into push platform, the spread speed would be fast and
accurate, which can cover the major people within several
seconds after the earthquake information report is completed
[1].
T
he cases of news report written by artificial intelligence
have not been rare. The news generated automatically has strong
competitive force in timeliness and accuracy, becoming the
natural advantages of developing to write news report by
artificial intelligence. However, currently, the artificial
intelligence fails to play its role in long report due to lack of
humanistic care and emotional intention, which are mainly
applied for news alerts. At the same time, the acquisition and
analysis ability on data of artificial intelligence coincides with
the demand of data news. As a consequence, this paper would
like to discuss the feasibility of artificial intelligence in the
application to data news field, and bring up with the applicable
model of data and images in natural disaster news report
processed by artificial intelligence.
B. Journals Reviewed
According to a study by Guan et al. [2] at China University
of Geosciences in 2021, it is shown that artificial intelligence
has been focused on solving seismic data processing and
comprehensive interpretation in the field of fluid deposit
exploration. In addition, it has been widely used in automatic
rock lithology and mineral classification, geological hazard risk
assessment, intelligent interpretation of remote sensing images,
natural seismic signal analysis and monitoring and prediction.
And a variety of machine learning and intelligent computing
methods have been able to meet the computing speed and
prediction accuracy required by the large volume of seismic
wave data, such as the combination of remote sensing image
processing, neural network, genetic algorithm and swarm
intelligence optimization algorithms to improve the image edge
recognition accuracy.
In the article “AI and IoT in Atmospheric Sciences”, it is
shown that “three types of intelligent analysis can be achieved
using the AI+IoT’ model”, namely real-time analysis, optimal
analysis and predictive analysis. “Meteorological researchers
are constantly trying to use this model for weather and climate
prediction, weather disaster warning, etc. The application of this
field is currently in its infancy, but in the future the integrated
use of AI and IoT has great potential and development space in
the field of meteorology.” Currently, this technology is mainly
applied in the fields of meteorological observation identification,
meteorological data processing, and weather and climate
analysis and forecasting [3].
According to Zhou's research [4], the current artificial
intelligence method can realize the three-dimensional analysis
and expression of pre-disaster warning, which provides a new
technical method for pre-disaster warning, and “disaster
management needs to capture real-time social media big data,
and the development and application of specific crisis
classification, entity classification and data aggregation
technology is urge, and there is also a need to present and
visualize social big data through maps”, and “artificial
intelligence has the characteristics of virtualization,
The
se authors contributed equally to this work
227
2023 15th International Conference on Computer Research and Development
978-1-6654-8750-4/23/$31.00 ©2023 IEEE
2023 15th International Conference on Computer Research and Development (ICCRD) | 978-1-6654-8750-4/23/$31.00 ©2023 IEEE | DOI: 10.1109/ICCRD56364.2023.10080745
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c
ontextualization and science, which is one of the best
technologies for post-disaster recovery” [4].
From the above literature, we can see that AI has played a
great role in the research related to natural disasters, and the
current technological development can fully support its
academic research and application. It is undeniable that the data
collection methods in the above research are worthy of our
research in this subject.
However, these research topics still focus on climate
prediction, pre disaster warning, etc., and do not visualize the
data in news reports. Few studies have combined the two and
conducted research on this subject.
The model in the above research is not applicable to news
reporting. The two most obvious problems are:
The images produced are too academic. The audience of
news reports is far more than that of researchers
engaged in natural disasters, so it is necessary to
describe natural disasters in the most intuitive and
concise words and images. High reading threshold will
directly reduce the communication efficiency.
The production process is too slow. The pursuit of
timeliness in news reporting, which spends too much
time on highly professional analysis, is not conducive to
high-quality communication.
In conclusion, the author believes that in the field of natural
disaster reporting, in order to facilitate understanding, ensure
accuracy and improve output efficiency, AI needs to reduce
professional analysis and use visual means of data visualization.
“The information obtained by the audience will have a greater
impact on the audience because of the preconception. The lack
of information caused by delayed reporting or even concealment
will leave room for rumors to spread. In serious cases, it will
even make the media lose the ability to set the agenda, causing
the spread of panic, reducing the credibility of the government,
and causing secondary harm to the society” [5]. When major
events such as natural disasters occur, news reports should not
be silenced at the new media end, but should highlight the
mobile priority strategy, and launch faster, more and better news
reports and financial media works at the two micro end[6].
To solve the above problems, we choose to use the crawler
algorithm to obtain public natural disaster related data.The
natural advantage of open data is that it is less professional and
easy to understand. Crawler algorithms can provide efficient
data retrieval for data visualization, speed up the production of
reports, and thus provide highly timely reports for the public.
II. I
NT
RODUCTION OF
D
AT
A
N
E
WS
A
. Multiplicity of Data Semantics
1) Data Storage
As it’s shown in figure 1, data exists as a symbol, and symbol
has its own semantics. Data can store ancient and past
information, preserving the instantaneous moments that have
been lost and helping us to track history. This is also the reason
for data can be compared for time. Meanwhile, the characteristic
of data can help innovation because innovation needs to focus
on previous experience. For example, the record of data museum
about historical dynasties can reflect this characteristic. In the
natural disaster report, the storage of data can help media and
the public to obtain past data so as to briefly summarize trends
to form a collection.
2) Fragmentation of Data
The data is divided into different segments by structure.
Each segment becomes the information carrier of different
topics or views that empowering data diversity. Due to the
fragmentation of data, it needs to guarantee the used data are
convenient for communicating, solving and dealing with when
using it. Meanwhile, when reporting natural disaster, this
characteristic can enhance the logic of report and describe this
disaster from multiple dimensions.
3) Abstractness of Data
Data is not the completed retention of the initial information.
The public can't see or feel some information through data,
instead they need to recover, process and use these scenarios
through retained data by thinking. Data refines the essential
characteristics from various matters of this event, and eliminates
the non-essential characteristics. Just because of this feature,
news report can extract concrete images from fact. As ancient
documents recording, it is impossible to record all figures, all
matters at all views, but to record their core and structure.
4) Granularity of Data
The granularity of data is similar to the pixels of pictures that
countless of granules composing the data and carrying
information. Meanwhile, the bigger of granularity is, the smaller
the information carried by the data, and the easier the data to be
understood. This characteristic of data is relevant with hierarchy
of natural disaster reports. For example, in the same natural
disaster event, some data only record the year of occurrence, but
some data accurate all events into seconds.
B. Journalists' Subjectivity: Two Paradigms
At present, there are two main paradigms of data news report.
Data driving news is that ones driven by data processing and
discussed from multiple technical perspectives. Data driving
news is the tool to analyze and filter huge amounts of news data.
It digs out news through integrating data.Data can drive media
field to produce contents, such as judging whether movies are
successful or not by scientific calculation. There are many risky
factors in the data driving news, such as the different quality of
data, the restriction of journalists subjectivity, and the
ignorance of relevance. The data are more emphasized in the
data driving news, while the news is restricted. Data supports the
narrative, but can't complete the narrative. Overemphasizing
data will lead the increment of the narrative results, and the role
of journalist editing will decrease.
The news report assisted by data has the feature of taking
data as foundation and assistance. News is still the main body,
of which the essence is narrative drivebased on data analysis,
and the data is just the means and methods of narrative
assistance. Telling a good story is still the ultimate and
fundamental demand of data news.Journalists must be the
managers of data. The CAR emerged in the early time
(computer assisted reporting, investigation, reference and
gathering talking) has been gradually replaced by data assisted
news reporting along with the coming of ABC (AI, Big data,
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F
ig. 1. Multiplicity of data semantics
St
rong convective weather data collection
obtain relevant data of
new strong convective
weather
Recognition and
acquisition of strong
convective information
t
he data of strong convective weather collected by this crawler
Store URL and
match it with
database
I
nitial URL
new URL
integrate and classify
images and data of
strong convective data
terminate crawler
m
ake judgement about that whether terminate
crawlerbased on the region,information
completionand other comprehensive conditions
F
ig. 2. The acquisition process of crawler’s intelligent disaster data
c
loud). To sum up, the main difference between two kinds of
data news report paradigms is the driving force. The data driving
news report is driven by data, while that of data assisted news
report is driven by narrative. The priority of dataand news
narrativeis different, which leading to more different points
mentioned as above. Currently, due to journalists are still
playing the main role in news reporting, the data assisted news
report has a more common paradigm in application.
III. A
P
PLICATION OF
A
R
TIFICIAL
I
N
TELLIGENCE IN
N
AT
URAL
D
I
SASTER
N
E
WS
R
E
PORT
F
I
ELD
F
or natural disaster related data, there are two main parts,
one is textual information data and the other is image data. One
of the main features of textual information data is that it is
relatively standardized relative to textual and professional data,
such as strong convective weather and the corresponding level,
dry weather and the level of drought degree. However, the
relative types are more complicated. Taking weather disasters as
an example, the typical ones are strong convection weather,
morning fog weather, typhoon weather, drought weather,
geological disaster weather, etc. The classification of each
disaster weather level has its own characteristics. On the other
hand, the image data is characterized by the fact that most of
them have the map templates of sea and land boundaries, and the
corresponding colors are used to distinguish different levels for
different kinds of weather hazards.
Based on the analysis of the data characteristics related to
natural disaster news, we also have a corresponding strategy in
the relevant processing algorithm, as follows.
1) Crawler algorithm: Taking weather disasters as an
example, most of the related warnings and real-time
data come from professional public portals on the
Internet. On the other hand, the corresponding
professional names of weather disasters are more fixed
and keyword retrieval is easier, so we can use crawler
algorithm to obtain real-time and wide range of first-
hand related data information.
Using crawler algorithm to obtain public natural disaster
data has the following advantages:
Improve the efficiency of natural disaster data retrieval
In the era of information explosion, data information is
complex and highly timely. This is especially true of
public data related to natural disasters. For example,
when a typhoon occurs in a certain area, yo
u
c
an
retrieve typhoon information on various portals,
professional websites, social platforms, etc., but if you
want to obtain accurate, open and authoritative target
disaster data at the first time, it will undoubtedly bring
huge workload. However, the use of the crawler
algorithm will greatly reduce the labor cost, and can
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m
ore efficiently and accurately obtain the relevant data
of the target natural disasters.
Crawlers can make data acquisition more accurate
The data of natural disasters contain a lot of professional
data and technical content. Many of the data content is
often not the material required by the press release, so
other professionals are required to conduct manual
identification and screening. However, the quality of the
news data that are subject to manual identification and
screening is not uniform. After using the crawler, we
can accurately screen the natural disaster data we need
after formulating standard screening rules.
2) Convolutional neural network recognition and
classification. Since natural disaster data will have
many image data, such as typhoon path map and
distribution map of strong convective weather, we can
apply the convolutional neural network of typical image
recognition to train the recognition of disaster data
images, or take the strong convective weather in
weather disaster as an example, on the one hand, we can
identify the type of disaster by the title name in the
image, and on the other hand, we can identify different
levels of disaster by the training color.
Specifically, the convolution algorithm has the
following advantages
Recognition of image data that can deal with natural
disasters: We all know that convolutional neural
networks are widely used in image recognition, speech
recognition and other fields. For natural disasters,
images are one of the most intuitive and critical data
elements. Basically, the data description of each natural
disaster can not be separated from images, so image
recognition is an indispensable function of natural
disaster identification.
Can cope with the complexity of natural disaster images
The image elements related to natural disasters are
complex and professional. Sea land boundary,
administrative region division, etc. are the basic
elements to describe the geographic information of
natural disasters. Different colors represent the severity
of natural disasters. Complex geographic information
will have the superposition of longitude and latitude
information. The convolution algorithm has been
developed and applied in image recognition and
processing, so the application of convolution algorithm
can be more conducive to the recognition and analysis
of weather data of natural disaster images.
A. The Key Points and Processes of Crawler Technology
Related to Disaster Weather News Data
Taking the strong convective weather as an example, shown
as figure 2, the collection process of strong convective weather
news data based on Internet crawler is as below:
1) Retrieve the strong convection data in the target area on
the official public data platform, generate the initial
URL, and taking the strong convection forecast from
China Weather Network
http://products.weather.com.cn as example, regard it as
initial URL. meanwhile associate and store the relevant
URL in the database.
2) Keep to retrieve based on new URL, key words of
relevant areas, date and other relevant data and website
of strong convection.
3) According to the completion of database combed and
issued by news, set the conditions of judgement. If meet
the condition, then terminate the crawler.
4) Classify images, data and others based on the algorithm
and the layout logic of news required for strong
convection weather forecast
1) Association Degree and Reliability of Association Rules
In the mining of meteorological disasters, early warning and
other related website data published by portals and official
websites on the Internet, an essential content factor is the
connection between various of data. And it is required to
segment and summarize these algorithms. Assume that the
meteorological disaster related data retrieval set we apply is
I={i1,i2,,im}, D is the database during disaster, then we can
indicate any meteorological disaster event by applying (ID, T).
ID is the corresponding event number, T={i1,i2,,in} represents
the data related to early warning of natural disaster. Therefore,
we have three definitions as below which is according to the
content of natural disasters:
Definition 1. The association degree of association rules,
representing that the ratio relationship between the natural
disaster data set containing both X and Y and the total natural
disaster data set. The expression is shown as formula (1):
sup() = |{: , }|/|D| (1)
Definition 2. The reliability of association rules,
representing the ratio relationship between the natural disaster
data set containing both X and Y and the natural disaster data
set only containing X. The general expression is as is shown as
formula (2):

= () = |{: , }|/|T: X , |
(2
)
D
efinition 3. When the association degree of project
collection support (X) is more than minimum value, then this
weather disaster data set is regarded as the data set of frequency
natural disaster.
2) Bayesian Network Algorithm Model Based on Natural
Disaster Data Analysis
For the Bayesian network algorithm model, we apply it to
the relevant models of natural disaster data digging. It is not hard
for us to promote it to natural disaster data digging according to
chain rule of probability(shown as formula (3)).
P(X
1
,…
,X
n
)=
(
|
1
,
2
,
−1
)
=1
(3
)
Among of it, x is the smallest non independent subset related
to natural disasters, and parent (Xi) {x1, X2,..., Xi - 1} is as
follows: P(Xi|X1,X2,,Xi -1)=P(Xi|Parent(Xi)). When we are
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t
alking about variable assignment, it can be promoted to
Bayesian network joint probability distribution based on natural
disaster data mining [7],which is shown as formula (4).
P
(X
1
,…
,X
n
)=
(
|
(

(
−1
)))
=1
=
|
(
(
))
=1
(4
)
W
e can design the Bayesian network retrieval process of the
Internet such as natural disaster portal and official website data
release:
1) Confirm the value scope of relevant scientific data of
natural disaster, such as the radar reflectivity, rainfall,
etc. when strong convective weather occurs, and the
wind speed value of typhoon weather and others. Then
classify according to specific characteristics.
Confirm the reliability relationship between different
data variables, which is used to build structural
schematic with direction but without ring, so as to build
the Bayesian Internet structure, that is: based on a
certain kind of logic order, make variable X satisfy the
condition of Parent(Xi) (i=1 ,2 , ,n).
2) Regulate or obtain local probability through learning
P(xi|Parent(Xi)) to confirm relevant parameters of
Bayesian model of natural disaster [7].
B. Image Recognition and Classification Algorithm of
Disaster Weather Warning Based on Convolution Network
1) The Algorithm Based on AlexNet Model’s Convolution
Network
There is 11 layers of convolution network in total in the deep
learning model brought up by Alex Krizhevshy, including
convolution layer, pooling layer and full connection layer.
The main steps of convolution and pooling are as below:
=
W−F+2
P
S
+1 (5
)
=
W−F
S
+1 (6
)
A
pplied to the deep learning related to disaster weather
images, we can obtain the AlexNet size of feature matrix based
on disaster weather convolution and pooling: W means the
height and width of feature matrix of disaster weather data, F
means the size of the convolution core /pooling core of disaster
weather images, P represents the number of disaster weather
feature matrix plus 0, and S means the step size of convolution
or pooling of disaster weather images. If S<=1, then formula (5)
works. If S>1, then formula (6) works [8].
2) Algorithm Based on ResNet Network
ResNet network is a neural network brought up in 2015.
Compared with traditional neural network structure (series
stacking of convolution layer and full connection layer
according to the simple logic), and along with the deepening of
the network, and in order to avoid performance degradation, the
concept of residual learning is added, meaning cross layer
connectionis stacked. Shown as below figure 3, the input of
Internet structure is X, output of F(X)+X, which avoids the
degradation of neural network to a certain extent.
Below are the two typical ResNet structures as BasicBlock
structure and BasicNect structure:
Shown as figure 4, the BasicBlockstructure is the scenario
with a shallow network, composed by two 3*3 convolution
layers. The number of paths is 64.
Shown as figure 5, the BasicNect structure is suitable for the
scenario with deeper network. First is 1*1 convolution layer
with the number of paths as 64. And next is 3*3 convolution
layer with the number of paths as 64. The last is 1*1 convolution
layer with the number of paths as 256 [8].
C. Image Recognition and Classification Application of
Disaster Weather Warning
Image recognition and classification of disaster weather
warning based on convolution network and shown as figure 6,
we take the strong convective weather, geological disaster, dry
weather and typhoon tracks as typical disaster weather cases.
Train the images of disaster weather, analyze and
extract the feature values of various of disaster weather.
Most of the disaster images are classified by color
representation, therefore, the extraction of color feature
is one of the most important standards.
For example, the amount of precipitation of strong
convection, the green, blue and pink color are taken as major
colors to distinguish the amount of precipitation. In the training
process of precipitation related samples, firstly, it is needed to
read the classification and benchmark of the basic image palette
(different publishing platforms, different countries and regions
will have different color representations of strong convective
weather).
The typhoon track in the national precipitation forecast map
describes the relevant positions in yellow and the longitude and
latitude of the map, and obtains real-time information of the
typhoon track by analyzing the corresponding colors.
Dry weather is described by white, yellow, orange, red and
brown as no drought, light drought, medium drought, heavy
drought and special drought.
Geological disasters are described in red, orange and yellow
as extremely high risk, high risk and higher risk.
S
tart
W
eight Layer
W
eight Layer
+
F(X)+X
Relu
X
Relu
X
Identity
F
ig. 3. ResNet structure
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S
tart
3*36
4
3*36
4
+
Relu
Relu
64-d
F
ig. 4. BasicBlock structure
S
tart
1*16
4
3*36
4
+
Relu
Relu
256-d
1*12
56
Relu
F
ig. 5. BotteNeck structure
C
arry out the training initialization on disaster images
based on the extracted disaster weather feature data, the
details include:
o initialize the structure and parameters of
strong convection, set the weight and
threshold value
o initialize the structure and parameters of
geographical disaster, set the weight and
threshold value
o initialize the structure and parameters of dry
weather, set the weight and threshold value
o initialize the structure and parameters of
typhoon track, set the weight and threshold
value
Carry out training based on existing feature and target
values, including
o Adjust the relevant threshold of strong
convection and calculate the error value
o Adjust the relevant threshold of geographical
disaster and calculate the error value
o Adjust the relevant threshold of dry weather
and calculate the error value
o Adjust the relevant threshold of typhoon track
and calculate the error value
Firstly, sort out the results after training the network,
and then test network respectively by strong convection,
geological disasters, dry weather and test images of
typhoons.
According to the test results, output the corresponding
strong convection identification results, geological
disaster identification results, dry weather identification
results and typhoon path identification results.
Above is typical disaster weather related research, which can
be extended to other related disaster warning, such as blizzard
forecast, hail forecast, haze warning and so on.
1) Application of Convolutional Neural Network Based
Disaster Weather Warning Classification and Recognition
Selection basis of sample data during the empirical analysis:
Firstly, the data of disaster weather are selected as samples
for analysis for two reasons: firstly, the data and images related
to disaster weather are more common, typical and representative.
Secondly, the types of weather are more complicated, such as
strong convection, morning fog, drought, etc. The samples are
richer and can be used as typical samples for crawlers and neural
network training. In refining the samples, that is, in selecting the
samples of disaster weather types, we cited the most common
disaster weather samples, that is, strong convective weather
(precipitation), dry weather, geological disasters, and typhoons.
Regarding the choice of training model structure:
convolutional + fully connected network) or using the classical
ResNet network, this training to typical convolutional neural
network as the main method, the specific model structure is
shown as Figure 7.
After three layers of convolution, the feature maps are
flattened and fully connected to output the score values of the
respective classification results, with the largest score being the
predicted category.
Since the main sample data is image data, our main model
structure is the typical convolutional neural network (based on
Training outcome:
We apply convolutional neural network to classify and
identify disaster weather warnings by using four types of
disaster weather warnings: strong convective weather
(precipitation), dry weather, national geological disaster, and
typhoon as experimental samples, the partical training daily
record is shown as Figure 8.
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Training
network
A
djust the relevant threshold of
strong convection and calculate
the error value
S
tart
En
d
F
eature extraction of image
parameters of strong convective
weather
F
eature extraction of image
parameters of typhoon paths
F
eature extraction of image
parameters of dry weather
A
djust the relevant threshold of
dry weather and calculate the error
value
Data analysis
and image
extraction of
disaster
weather
features
A
djust the relevant threshold of
geological disaster and calculate
the error value
A
djust the relevant threshold of
typhoon paths and calculate the
error value
T
est the network after training
data output
i
nitialize the structure and
parameter of strong convection ,
set weights and thresholds
i
nitialize the structure and
parameter of geological disaster,
set weights and thresholds
i
nitialize the structure and
parameter of typhoon paths, set
weights and thresholds
i
nitialize the structure and
parameter of dry weather, set
weights and thresholds
Training
initialization
of disaster
images
tr
ain the disaster weather
images
F
eature extraction of image
parameters of geological disaste
r
esult output of strong
convective image recognition
r
esult output of geological
disaster image recognition
r
esult output of disaster weather
image recognition
r
esult output of typhoon paths
image recognition
F
ig. 6.
The flow chart of images recognition and classification of disaster weather warning based on convolution network
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F
ig. 7. ResNet structure
F
ig. 8. Partical training daily record
D. A
nalysis and Conclusion
Through the algorithm study in 3.1-3.3, as well as the
training and analysis with disaster weather as the sample, we can
conclude that the data of disaster news reports are related to
images.
(1) The crawler algorithm logic for early warning and real-
time data of disaster images can be further analyzed according
to the specific time date and geographical area in addition to the
type name of the disaster, so that real-time and predicted data
and images can be acquired for disasters in any administrative
area, and the timeliness of the material is improved, which helps
the rapid and automatic generation of news reports.
(2) By training a typical convolutional neural network for
disaster weather, we train and classify different disaster weather,
and by the relevant disaster weather features (e.g., strong
convective weather names as well as color intensity), we can
quickly classify the disaster weather. Extending to the
classification and recognition of other types of disaster-related
data and images, we can perform relevant training to achieve
artificial intelligence recognition of different disasters based on
standardized title names as well as image features.
(3) Absolutely, the crawler algorithm and typical
Convolutional neural network training have some limitations
related to disaster news reporting. First of all, the crawler
algorithm, we can only have access to publicly available
weather-related website data, for overseas data, unpublished
related weather information, we cannot get. On the other hand,
our typical convolutional neural network based recognition
classification also has some limitations, for example, the color
standard (palette) of disaster images in different countries will
be different to some extent, so that the classification recognition
will encounter related problems when dealing with publicly
available disaster warning and real-time images from different
countries, so we can only recognize training and classification
for images with consistent disaster image palette.
IV. F
UT
URE
D
EV
ELOPMENT
:
C
HAL
LENGES AND
O
P
PORTUNITIES
A
. Challenges: Artificial Intelligence Abuse
1) Law Risk
Along with the social development and progress of
technology, the artificial intelligence will gradually replace
many human positions, and the human reply more and more on
artificial intelligence. As a kind of high-tech, the damages
brought by illegal and criminal activities carried out by using
artificial intelligence technology are great. If these behaviors
and activities are not stopped by law in time, it will become one
of the factors leading social instability. The artificial intelligence
technology has been gradually rooted in many fields, resulting
in more and more ethical and legal disputes. For example, in the
production of data news report, the arguments on the ownership
of copyright and right of author of artificial intelligence are
never stopped.
In recent years, the shortcomings of artificial intelligence
technology have been gradually emerged in the news practice,
and the pursuit of news production subject to interests is
increasing day by day. The shortcomings of artificial intelligence
and supervision regulation of press field, and the incomplete
ethical system of news profession have all led to the prominence
of new ethics and legal issues in the process of combining
artificial intelligence with journalism. As a consequence, we
need to re consider the solution of news ethics [9].
2) Deep Forgery and Ethical Issues
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D
eep forgery is a forgery technology generated on the
foundation of artificial intelligence deep learning, which mainly
forges other people's facial expressions and voices in real time
by using of artificial intelligence technology, and combines it
into new videos. In December,2017, a user named deepfake
posted a pornographic video impersonating a well-known
Hollywood actress on a foreign website Reddit, instantly
activating a carnival on the Internet. Deep forgery has been
applied in the film and television industry a few years ago,
creating special efforts scenes and makeup to achieve more
refined communication effects. However, at the same time, it
also provides opportunities for people with ulterior motives [10].
B. Opportunity: Overcome Difficulties
1) Automatic Writing of Disaster Weather News Based on
Artificial Intelligence
Based on current deep learning technology, we can sort out
and recognize the real-time disaster warning data and carry out
a series of classification. However, if edit these materials into
formal news that can be output directly so as to realize automatic
writing, a certain of technical difficulties still exists. Below is
mainly to carry out analysis in two perspectives:
News writing has a certain degree of subjective thinking
and opinions of the writer, so as the disaster weather
news. Except information related to disaster warning, it
may still contain the prevention measurements, notices,
and subjective opinions under detailed situation. Taking
the most common strong convective weather as the
sample, except reporting the forecast, strength and
others of rainfall, it still needs to prepare some special
prevention measurements combined with the terrain,
topography, crowd density, morning and evening peak
travel of a specific area. All of these have opinions and
evaluation of subjective thinking. Therefore, the writing
of disaster weather news still needs a certain degree of
artificial assessment and modification.
Achieve the technical challenges needed by automatic
writing. Based on above analysis, we know the key
point of realizing automatic writing is to realize the
consideration of factors with subjective thinking
patterns, which involve in humanities, environment,
geography, politics and other elements. Therefore,
sufficient data samples are needed, and then an
algorithm that can simulate news schemes with
appropriate subjective thinking should be available. So
the news writers can be completely replaced and editing
of news.
2) Timeliness of Disaster News
As we know, the special feature of disaster early warning
news and other news is that they have a certain of timeliness.
Due to it is involved in paying attentions to the safety of relevant
groups, and leaving enough time to make corresponding
prevention measures, therefore, it certainly has a request of
timeliness of disaster news.
First of all, with the help of artificial intelligence, compared
with traditional news writing, the semi self-help mode of early
warning of disaster weather news has been certainly increased
efficiency. However, with the special timeliness request of
disaster weather, it still faces the challenge of improving
efficiency. The details include below aspects:
Improve the efficiency of computers calculation
efficiency and performance. Due to a huge amount of
historical data and samples is needed, deep learning of
disaster news requires large amounts of calculation
resource. Therefore, in the perspective of test and
business operation, both need higher amount of
calculation resources.
Optimize storage structure and logic. For example,
based on the advantages of cloud calculation, apply
objective storage to separately store static data and
dynamic data. Meanwhile, realize the separation of read
and writing.
Due to the disaster weather forecast has a request of
timeliness even to minutes and seconds, how to
optimize the algorithm and logic of deep learning, how
to quickly obtain sample data and complete initial
writing of news in the soonest time are the challenges
that need to be consistently optimized and improved.
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